SoroushVahidi/scidocs huggingface dataset:
The Ideal Elf: Identity Exploration in World of Warcraft
(2007)

In this study, we examine the identity exploration possibilities presented by online multiplayer games in which players use graphics tools and character-creation software to construct an avatar, or character. We predicted World of Warcraft players would create their main character more similar to their ideal self than the players themselves were. Our results support this idea; a sample of players rated their character as having more favorable attributes that were more favorable than their own self-rated attributes. This trend was stronger among those with lower psychological well-being, who rated themselves comparatively lower than they rated their character. Our results suggest that the game world allows players the freedom to create successful virtual selves regardless of the constraints of their actual situation.
Detection of Atrial Fibrillation using model-based ECG analysis
(2008)

Atrial fibrillation (AF) is an arrhythmia that can lead to several patient risks. This kind of arrhythmia affects mostly elderly people, in particular those who suffer from heart failure (one of the main causes of hospitalization). Thus, detection of AF becomes decisive in the prevention of cardiac threats. In this paper an algorithm for AF detection based on a novel algorithm architecture and feature extraction methods is proposed. The aforementioned architecture is based on the analysis of the three main physiological characteristics of AF: i) P wave absence ii) heart rate irregularity and iii) atrial activity (AA). Discriminative features are extracted using model-based statistic and frequency based approaches. Sensitivity and specificity results (respectively, 93.80% and 96.09% using the MIT-BIH AF database) show that the proposed algorithm is able to outperform state-of-the-art methods.
Index Financial Time Series Based on Zigzag-Perceptually Important Points
(2010)

Problem statement: Financial time series were usually large in size, unstructured and of high dimensionality. Since, the illustration of fin a cial time series shape was typically characterize d by a few number of important points. These important p oints moved in zigzag directions which could form technical patterns. However, these important p oints exhibited in different resolutions and diffic ult to determine. Approach: In this study, we proposed novel methods of financ i l time series indexing by considering their zigzag movement. The methods c onsist of two major algorithms: first, the identification of important points, namely the Zigz ag-Perceptually Important Points (ZIPs) identification method and next, the indexing method namely Zigzag based M-ary Tree (ZM-Tree) to structure and organize the important points. Results: The errors of the tree building and retrieving compared to the original time series increased when t important points increased. The dimensionality reduction using ZM-Tree based on tree pruning and n umber of retrieved points techniques performed better when the number of important points increase d. Conclusion: Our proposed techniques illustrated mostly acceptable performance in tree op rations and dimensionality reduction comparing to existing similar technique like Specialize Binar y Tree (SB-Tree).
Towards High Performance Video Object Detection
(2018)

There has been significant progresses for image object detection in recent years. Nevertheless, video object detection has received little attention, although it is more challenging and more important in practical scenarios. Built upon the recent works [37, 36], this work proposes a unified approach based on the principle of multi-frame end-to-end learning of features and cross-frame motion. Our approach extends prior works with three new techniques and steadily pushes forward the performance envelope (speed-accuracy tradeoff), towards high performance video object detection.
Fusion of Stereo Vision for Pedestrian Recognition using Convolutional Neural Networks
(2017)

Pedestrian detection is a highly debated issue in the scientific community due to its outstanding importance for a large number of applications, especially in the fields of automotive safety, robotics and surveillance. In spite of the widely varying methods developed in recent years, pedestrian detection is still an open challenge whose accuracy and robustness has to be improved. Therefore, in this paper, we focus on improving the classification component in the pedestrian detection task on the Daimler stereo vision data set by adopting two approaches: 1) by combining three image modalities (intensity, depth and flow) to feed a unique convolutional neural network (CNN) and 2) by fusing the results of three independent CNNs.
Millimeter-Wave Technology for Automotive Radar Sensors in the 77 GHz Frequency Band
(2012)

The market for driver assistance systems based on millimeter-wave radar sensor technology is gaining momentum. In the near future, the full range of newly introduced car models will be equipped with radar based systems which leads to high volume production with low cost potential. This paper provides background and an overview of the state of the art of millimeter-wave technology for automotive radar applications, including two actual silicon based fully integrated radar chips. Several advanced packaging concepts and antenna systems are presented and discussed in detail. Finally measurement results of the fully integrated radar front ends are shown.
Massively parallel Monte Carlo for many-particle simulations on GPUs
(2013)

Current trends in parallel processors call for the design of efficient massively parallel algorithms for scientific computi ng. Parallel algorithms for Monte Carlo simulations of thermodynamic en s mbles of particles have received little attention becaus e of the inherent serial nature of the statistical sampling. In this paper, we present a massively parallel method that obeys det ailed balance and implement it for a system of hard disks on the GPU. We repro duce results of serial high-precision Monte Carlo runs to ve rify the method. This is a good test case because the hard disk equa tion of state over the range where the liquid transforms into the solid is particularly sensitive to small deviations away from the balance conditions. On a Tesla K20, our GPU implementation e xecutes over one billion trial moves per second, which is 148 times fa ster than on a single Intel Xeon E5540 CPU core, enables 27 tim es better performance per dollar, and cuts energy usage by a fac tor of 13. With this improved performance we are able to calcu l te the equation of state for systems of up to one million hard dis ks. These large system sizes are required in order to probe th e nature of the melting transition, which has been debated for the las t forty years. In this paper we present the details of our comp utational method, and discuss the thermodynamics of hard disks separa t ly in a companion paper.
CNN-based ranking for biomedical entity normalization
(2017)

Most state-of-the-art biomedical entity normalization systems, such as rule-based systems, merely rely on morphological information of entity mentions, but rarely consider their semantic information. In this paper, we introduce a novel convolutional neural network (CNN) architecture that regards biomedical entity normalization as a ranking problem and benefits from semantic information of biomedical entities. The CNN-based ranking method first generates candidates using handcrafted rules, and then ranks the candidates according to their semantic information modeled by CNN as well as their morphological information. Experiments on two benchmark datasets for biomedical entity normalization show that our proposed CNN-based ranking method outperforms traditional rule-based method with state-of-the-art performance. We propose a CNN architecture that regards biomedical entity normalization as a ranking problem. Comparison results show that semantic information is beneficial to biomedical entity normalization and can be well combined with morphological information in our CNN architecture for further improvement.
Trending topic prediction on social network
(2013)

The fast information sharing on social network services generates more than thousands of topics every day. It is extremely important for business organizations and administrative decision makers to learn the popularity of these topics as quickly as possible. In this paper, we propose a prediction mode based on SVM with features of three subsets: quantity specific features, quality and user specific features which supplement each other. Furthermore, we divide topic data into time slices which is used as a unit of feature construction. Our findings suggest that the capability of our prediction model outperforms previous methods and also reveals that subsets of features play different role in the prediction of trending topics.
NoSQL Database: New Era of Databases for Big data Analytics - Classification, Characteristics and Comparison
(2013)

Digital world is growing very fast and become more complex in the volume (terabyte to petabyte), variety (structured and un-structured and hybrid), velocity (high speed in growth) in nature. This refers to as ‘Big Data’ that is a global phenomenon. This is typically considered to be a data collection that has grown so large it can’t be effectively managed or exploited using conventional data management tools: e.g., classic relational database management systems (RDBMS) or conventional search engines. To handle this problem, traditional RDBMS are complemented by specifically designed a rich set of alternative DBMS; such as NoSQL, NewSQL and Search-based systems. This paper motivation is to provide classification, characteristics and evaluation of NoSQL databases in Big Data Analytics. This report is intended to help users, especially to the organizations to obtain an independent understanding of the strengths and weaknesses of various NoSQL database approaches to supporting applications that process huge volumes of data.
Towards parameter-free data mining
(2004)

Most data mining algorithms require the setting of many input parameters. Two main dangers of working with parameter-laden algorithms are the following. First, incorrect settings may cause an algorithm to fail in finding the true patterns. Second, a perhaps more insidious problem is that the algorithm may report spurious patterns that do not really exist, or greatly overestimate the significance of the reported patterns. This is especially likely when the user fails to understand the role of parameters in the data mining process.Data mining algorithms should have as few parameters as possible, ideally none. A parameter-free algorithm would limit our ability to impose our prejudices, expectations, and presumptions on the problem at hand, and would let the data itself speak to us. In this work, we show that recent results in bioinformatics and computational theory hold great promise for a parameter-free data-mining paradigm. The results are motivated by observations in Kolmogorov complexity theory. However, as a practical matter, they can be implemented using any off-the-shelf compression algorithm with the addition of just a dozen or so lines of code. We will show that this approach is competitive or superior to the state-of-the-art approaches in anomaly/interestingness detection, classification, and clustering with empirical tests on time series/DNA/text/video datasets.
Do Differences in Password Policies Prevent Password Reuse?
(2017)

Password policies were originally designed to make users pick stronger passwords. However, research has shown that they often fail to achieve this goal. In a systematic audit of the top 100 web sites in Germany, we explore if diversity in current real-world password policies prevents password reuse. We found that this is not the case: we are the first to show that a single password could hypothetically fulfill 99% of the policies under consideration. This is especially problematic because password reuse exposes users to similar risks as weak passwords. We thus propose a new approach for policies that focuses on password reuse and respects other websites to determine if a password should be accepted. This re-design takes current user behavior into account and potentially boosts the usability and security of password-based authentication.
Review on Machine Learning Techniques for Automatic Segmentation of Liver Images
(2013)

The segmentation of liver from abdominal images has gained huge importance in medical imaging field since it is fundamental step to diagnose liver tumor and other liver diseases, liver volume measurement and 3D liver volume rendering. In this paper, recent automatic methods for liver image segmentation proposed in literature are reviewed. Generally, Liver image segmentation techniques can be semi-automatic and fully automatic. Under fully automatic segmentation category, various methods, approaches, recent advances and related problems has been explained. Brief summary of all discussed methods has been provided. Finally it is concluded that liver image segmentation is still an open problem since various drawbacks of proposed methods can be addressed. Keywords— Liver image Segmentation, Automatic Segmentation, Neural Network, Support Vector Machine, Clustering, Hybrid, Review.
Vacuum-assisted thermal annealing of CH3NH3PbI3 for highly stable and efficient perovskite solar cells.
(2015)

Solar cells incorporating lead halide-based perovskite absorbers can exhibit impressive power conversion efficiencies (PCEs), recently surpassing 15%. Despite rapid developments, achieving precise control over the morphologies of the perovskite films (minimizing pore formation) and enhanced stability and reproducibility of the devices remain challenging, both of which are necessary for further advancements. Here we demonstrate vacuum-assisted thermal annealing as an effective means for controlling the composition and morphology of the CH(3)NH(3)PbI(3) films formed from the precursors of PbCl(2) and CH(3)NH(3)I. We identify the critical role played by the byproduct of CH(3)NH(3)Cl on the formation and the photovoltaic performance of the perovskite film. By completely removing the byproduct through our vacuum-assisted thermal annealing approach, we are able to produce pure, pore-free planar CH(3)NH(3)PbI(3) films with high PCE reaching 14.5% in solar cell device. Importantly, the removal of CH(3)NH(3)Cl significantly improves the device stability and reproducibility with a standard deviation of only 0.92% in PCE as well as strongly reducing the photocurrent hysteresis.
Interpreting BLEU/NIST Scores: How Much Improvement do We Need to Have a Better System?
(2004)

Automatic evaluation metrics for Machine Translation (MT) systems, such as BLEU and the related NIST metric, are becoming increasingly important in MT. Yet, their behaviors are not fully understood. In this paper, we analyze some flaws in the BLEU/NIST metrics. With a better understanding of these problems, we can better interpret the reported BLEU/NIST scores. In addition, this paper reports a novel method of calculating the confidence intervals for BLEU/NIST scores using bootstrapping. With this method, we can determine whether two MT systems are significantly different from each other.
Low-power flyback converter with synchronous rectification for a system with AC power distribution
(2002)

IEC 1000-3-2 regulations impose a reduced harmonic content on any converter with an input power higher than 75 W. However, if the power architecture of the system is based on small on-board converters, and the total power is higher than 75 W, IEC regulations must be fulfilled although each individual converter need not comply with the regulations. In this paper, one of the different possible solutions is presented. Each on-board converter has an active input current shaper (AICS) in order to reduce the input current harmonic content of each converter and, hence, to comply with IEC 1000-3-2 regulations. Moreover, two different types of AICSs were compared: the conventional one and a new type of AICS based on a full-wave rectifier.
An analysis of 3D point cloud reconstruction from light field images
(2016)

Current methodologies for the generation of 3D point cloud from real world scenes rely upon a set of 2D images capturing the scene from several points of view. Novel plenoptic cameras sample the light field crossing the main camera lens creating a light field image. The information available in a plenoptic image must be processed in order to render a view or create the depth map of the scene. This paper analyses a method for the reconstruction of 3D models. The reconstruction of the model is obtained from a single image shot. Exploiting the properties of plenoptic images, a point cloud is generated and compared with a point cloud of the same object but generated with a different plenoptic camera.
Multi-robot long-term persistent coverage with fuel constrained robots
(2015)

In this paper, we present an algorithm to solve the Multi-Robot Persistent Coverage Problem (MRPCP). Here, we seek to compute a schedule that will allow a fleet of agents to visit all targets of a given set while maximizing the frequency of visitation and maintaining a sufficient fuel capacity by refueling at depots. We also present a heuristic method to allow us to compute bounded suboptimal results in real time. The results produced by our algorithm will allow a team of robots to efficiently cover a given set of targets or tasks persistently over long periods of time, even when the cost to transition between tasks is dynamic.
Toward Scalable Verification for Safety-Critical Deep Networks
(2018)

The increasing use of deep neural networks for safety-critical applications, such as autonomous driving and flight control, raises concerns about their safety and reliability. Formal verification can address these concerns by guaranteeing that a deep learning system operates as intended, but the state-of-the-art is limited to small systems. In this work-in-progress report we give an overview of our work on mitigating this difficulty, by pursuing two complementary directions: devising scalable verification techniques, and identifying design choices that result in deep learning systems that are more amenable to verification. ACM Reference Format: Lindsey Kuper, Guy Katz, Justin Gottschlich, Kyle Julian, Clark Barrett, and Mykel J. Kochenderfer. 2018. Toward Scalable Verification for SafetyCritical Deep Networks. In Proceedings of SysML Conference (SysML). ACM, New York, NY, USA, 3 pages.
Identifying careless responses in survey data.
(2012)

When data are collected via anonymous Internet surveys, particularly under conditions of obligatory participation (such as with student samples), data quality can be a concern. However, little guidance exists in the published literature regarding techniques for detecting careless responses. Previously several potential approaches have been suggested for identifying careless respondents via indices computed from the data, yet almost no prior work has examined the relationships among these indicators or the types of data patterns identified by each. In 2 studies, we examined several methods for identifying careless responses, including (a) special items designed to detect careless response, (b) response consistency indices formed from responses to typical survey items, (c) multivariate outlier analysis, (d) response time, and (e) self-reported diligence. Results indicated that there are two distinct patterns of careless response (random and nonrandom) and that different indices are needed to identify these different response patterns. We also found that approximately 10%-12% of undergraduates completing a lengthy survey for course credit were identified as careless responders. In Study 2, we simulated data with known random response patterns to determine the efficacy of several indicators of careless response. We found that the nature of the data strongly influenced the efficacy of the indices to identify careless responses. Recommendations include using identified rather than anonymous responses, incorporating instructed response items before data collection, as well as computing consistency indices and multivariate outlier analysis to ensure high-quality data.
Simultaneous administration of the Rosenberg Self-Esteem Scale in 53 nations: exploring the universal and culture-specific features of global self-esteem.
(2005)

The Rosenberg Self-Esteem Scale (RSES) was translated into 28 languages and administered to 16,998 participants across 53 nations. The RSES factor structure was largely invariant across nations. RSES scores correlated with neuroticism, extraversion, and romantic attachment styles within nearly all nations, providing additional support for cross-cultural equivalence of the RSES. All nations scored above the theoretical midpoint of the RSES, indicating generally positive self-evaluation may be culturally universal. Individual differences in self-esteem were variable across cultures, with a neutral response bias prevalent in more collectivist cultures. Self-competence and self-liking subscales of the RSES varied with cultural individualism. Although positively and negatively worded items of the RSES were correlated within cultures and were uniformly related to external personality variables, differences between aggregates of positive and negative items were smaller in developed nations. Because negatively worded items were interpreted differently across nations, direct cross-cultural comparisons using the RSES may have limited value.
Assessing psychopathic attributes in a noninstitutionalized population.
(1995)

The present study examined antisocial dispositions in 487 university students. Primary and secondary psychopathy scales were developed to assess a protopsychopathic interpersonal philosophy. An antisocial action scale also was developed for purposes of validation. The primary, secondary, and antisocial action scales were correlated with each other and with boredom susceptibility and disinhibition but not with experience seeking and thrill and adventure seeking. Secondary psychopathy was associated with trait anxiety. Multiple regression analysis revealed that the strongest predictors of antisocial action were disinhibition, primary psychopathy, secondary psychopathy, and sex, whereas thrill and adventure seeking was a negative predictor. This argues against a singular behavioral inhibition system mediating both antisocial and risk-taking behavior. These findings are also consistent with the view that psychopathy is a continuous dimension.
Wound healing activity of the fruit skin of Punica granatum.
(2013)

The skin of the fruit and the bark of Punica granatum are used as a traditional remedy against diarrhea, dysentery, and intestinal parasites. The fruit skin extract of P. granatum was tested for its wound healing activity in rats using an excision wound model. The animals were divided into three groups of six each. The experimental group of animals was topically treated with P. granatum at a dose of 100 mg/kg every day for 15 days, while the controls and standard group animals were treated with petroleum jelly and mupirocin ointment, respectively. Phytochemical analysis of the extract revealed the presence of saponins, triterpenes, tannins, alkaloids, flavonoids, and cardiac glycosides. Extract-treated animals exhibited 95% reduction in the wound area when compared with controls (84%), which was statistically significant (P<.01). The extract-treated wounds were found to epithelize faster compared with controls. The hydroxyproline content of extract-treated animals was significantly higher than controls (P<.05). The fruit skin extract did not show any antimicrobial activity against the microrganisms tested. P. granatum promotes significant wound healing in rats and further evaluation of this activity in humans is suggested.